Main Reference PaperAn Android Malware Detection Approach using Community Structures of Weighted Function Call graphs, June 2017 [R].
  • A malware detection algorithm for android is proposed in which android applications are decompiled and the decompiled files are processed to generates the community structure with features that are used to detect using efficient classification algorithm.

Description
  • A malware detection algorithm for android is proposed in which android applications are decompiled and the decompiled files are processed to generates the community structure with features that are used to detect using efficient classification algorithm.

  • To utilize machine learning approach for malware detection.

  • To improve the detection accuracy.

Aim & Objectives
  • To utilize machine learning approach for malware detection.

  • To improve the detection accuracy.

  • Efficient algorithms are designed by using semantic-based features.

Contribution
  • Efficient algorithms are designed by using semantic-based features.

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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